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- Chapter 1: Introduction
- 1.1 Forecasting, Classification, and Dimensionality Reduction
- 1.2 Synergies
- 1.3 The Interface Problems
- 1.4 Plan of the Book
- I: Econometric Foundations
- Chapter 2: What Are Neural Networks?
- 2.1 Linear Regression Model
- 2.2 GARCH Nonlinear Models
- 2.3 Model Typology
- 2.4 What Is A Neural Network?
- 2.5 Neural Network Smooth-Transition Regime Switching Models
- 2.6 Nonlinear Principal Components: Intrinsic Dimensionality
- 2.7 Neural Networks and Discrete Choice
- 2.8 The Black Box Criticism and Data Mining
- 2.9 Conclusion
- Chapter 3: Estimation of a Network with Evolutionary Computation
- 3.1 Data Preprocessing
- 3.2 The Nonlinear Estimation Problem
- 3.3 Repeated Estimation and Thick Models
- 3.4 MATLAB Examples: Numerical Optimization and Network Performance
- 3.5 Conclusion
- Chapter 4: Evaluation of Network Estimation
- 4.1 In-Sample Criteria
- 4.2 Out-of-Sample Criteria
- 4.3 Interpretive Criteria and Significance of Results
- 4.4 Implementation Strategy
- 4.5 Conclusion
- II: Applications and Examples
- Chapter 5: Estimating and Forecasting with Artificial Data
- 5.1 Introduction
- 5.2 Stochastic Chaos Model
- 5.3 Stochastic Volatility/Jump Diffusion Model
- 5.4 The Markov Regime Switching Model
- 5.5 Volatility Regime Switching Model
- 5.6 Distorted Long-Memory Model
- 5.7 Black-Sholes Option Pricing Model: Implied Volatility Forecasting
- 5.8 Conclusion
- Chapter 6: Times Series: Examples from Industry and Finance
- 6.1 Forecasting Production in the Automotive Industry
- 6.2 Corporate Bonds: Which Factors Determine the Spreads?
- 6.3 Conclusion
- Chapter 7: Inflation and Deflation: Hong Kong and Japan
- 7.1 Hong Kong
- 7.2 Japan
- 7.3 Conclusion
- Chapter 8: Classification: Credit Card Default and Bank Failures
- 8.1 Credit Card Risk
- 8.2 Banking Intervention
- 8.3 Conclusion
- Chapter 9: Dimensionality Reduction and Implied Volatility Forecasting
- 9.1 Hong Kong
- 9.2 United States
- 9.3 Conclusion
This book explores the intuitive appeal of neural networks and the genetic algorithm in finance. It demonstrates how neural networks used in combination with evolutionary computation outperform classical econometric methods for accuracy in forecasting, classification and dimensionality reduction.
McNelis utilizes a variety of examples, from forecasting automobile production and corporate bond spread, to inflation and deflation processes in Hong Kong and Japan, to credit card default in Germany to bank failures in Texas, to cap-floor volatilities in New York and Hong Kong.
- Offers a balanced, critical review of the neural network methods and genetic algorithms used in finance
- Includes numerous examples and applications
- Numerical illustrations use MATLAB code and the book is accompanied by a website
Upper division undergraduates and MBA students, as well as the rapidly growing number of financial engineering programs, whose curricula emphasize quantitative applications in financial economics and markets
- No. of pages:
- © Academic Press 2005
- 22nd December 2004
- Academic Press
- Hardcover ISBN:
- eBook ISBN:
"This book clarifies many of the mysteries of Neural Networks and related optimization techniques for researchers in both economics and finance. It contains many practical examples backed up with computer programs for readers to explore. I recommend it to anyone who wants to understand methods used in nonlinear forecasting." -- Blake LeBaron, Professor of Finance, Brandeis University "An important addition to the select collection of books on financial econometrics, Paul Mcnelis' volume, Neural Networks in Finance, serves as an important reference on neural network models of nonlinear dynamics as a practical econometric tool for better decision-making in financial markets." -- Roberto S. Mariano, Dean of School of Economics and Social Sciences & Vice-Provost for Research, Singapore Management University; Professor Emeritus of Economics, University of Pennsylvania "This book represents an impressive step forward in the exposition and application of evolutionary computational tools. The author illustrates the potency of evolutionary computational tools through multiple examples, which contrast the predictive outcomes from the evolutionary approach with others of a linear and general non-linear variety. The book will be of utmost appeal to both academics throughout the social sciences as well as practitioners, especially in the area of finance." -- Carlos Asilis, Portfolio Manager, VegaPlus Capital Partners; formerly Chief Investment Strategist, JPMorgan Chase "...an excellent, easy-to read introduction to the math behind neural networks." - Financial Engineering News
Robert Bendheim Professor of International Economic and Financial Policy at Fordham University Graduate School of Business. Professor of Economics at Georgetown University until 2004.
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